internal_link_recommend

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description provides extensive behavioral details beyond the annotations: it crawls from start_url, uses GSC for striking-distance targets, ranks by lexical relevance and internal in-degree, skips already linking pages, balances anchor text, and never suggests nofollow. This aligns with the readOnlyHint and indicates no destructive behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, front-loaded with the core purpose, and every sentence adds meaningful detail without redundancy. It efficiently covers the tool's functionality, constraints, and caveats.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite lacking an output schema, the description hints at the output format ('source page -> target page, with anchor text'). Given the complexity of the tool (9 parameters, custom algorithm) and rich annotations, the description is largely complete. However, it could briefly mention the response structure or further clarify the 'striking-distance' concept for total completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the description adds value by explaining the algorithm context, e.g., 'default position 8-20' for position_min/max and 'default 28' for days. While the schema already describes each parameter, the description integrates them into the overall process, helping an agent interpret how parameters affect the recommendation logic.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Recommend specific internal links (source page -> target page, with anchor text) from high-authority pages to striking-distance pages.' This is a specific verb-resource combination that distinguishes it from sibling tools like gsc_search_analytics or internal_link_graph, which have different focuses.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool: when you need internal linking recommendations based on authority and striking-distance targets. It states it is 'free; read-only' and does not guarantee ranking change, setting expectations. However, it does not explicitly compare to alternatives or state when not to use it, missing some guidance for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.